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kcpRS  

Kernel Change Point Detection on the Running Statistics
View on CRAN: Click here


Download and install kcpRS package within the R console
Install from CRAN:
install.packages("kcpRS")

Install from Github:
library("remotes")
install_github("cran/kcpRS")

Install by package version:
library("remotes")
install_version("kcpRS", "1.1.1")



Attach the package and use:
library("kcpRS")
Maintained by
Kristof Meers
[Scholar Profile | Author Map]
First Published: 2019-05-06
Latest Update: 2023-10-25
Description:
The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, ). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.
How to cite:
Kristof Meers (2019). kcpRS: Kernel Change Point Detection on the Running Statistics. R package version 1.1.1, https://cran.r-project.org/web/packages/kcpRS. Accessed 10 May. 2025.
Previous versions and publish date:
1.0.0 (2019-05-06 11:00), 1.1.0 (2023-01-20 00:30)
Other packages that cited kcpRS R package
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Complete documentation for kcpRS
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